Analog circuit fault diagnosis is equivalent to mode recognition in essence, and the key is to seek a relationship between feature extraction and mode criterion function. Therefore, how to seek potential fault feature factors from seemingly complicated test data and conduct correct judgment and recognition on the fault mode on this basis is deemed as a significant research subject in the field of analog circuit testing.
Through development of the analog circuit fault diagnosis over the past decade, the achievement which has been achieved is varied and shows diversity, and the new research results continually come out. To summarize the technologies adopted in fault diagnosis, a fault feature extracting method based on statistical theory and wavelet analysis, and a fault mode recognition method based on neural network and support vector machine are widely applied at present. These aspects have great impact on promoting the development of the analog circuit fault diagnosis technologies. However, the analog circuit fault diagnosis technologies are still in development so far due to the large knowledge scope of analog circuit fault diagnosis design, deficiency of fault models and the binding character of the methods thereof.
Because the measurable nodes of the analog circuit are limited, and only one node at an output terminal serves as the measurable node under most circumstances. The test data collected under this case are usually mixed data of various independent sources, and the feature factors thereof are implied deeply. When diagnosing these circuits, a very expensive amount of computations for classification is caused usually if the original data collected is directly fed to a classifier for classification, which is difficult to implement, and also has poor classification effect, and high erroneous judgment ratio. Independent factors from system bottom are not single; moreover, these are invisible independent sources (i.e., blind sources) for a ten final user. A technology regarding blind source processing at current is mainly applied to the field of voice recognition, and its application prerequisite is multi-channel measured data source. Therefore, extracting the feature factors of the circuit measured with using a single testable node cannot be directly implemented by a blind source separation technology.